The Sources of Risk Spillovers Among U.S. REITs : Financial Characteristics and Regional Proximityjournal paperhttps://www.alexandria.unisg.ch/publications/226012enurn:ISSN:1080-8620doi:10.1111/1540-6229.12060Real Estate EconomicsIn this paper, we estimate the risk spillovers among 74 U.S. REITs using the statedependent sensitivity value-at-risk (SDSVaR) approach. This methodology allows for the quantification of the spillover size as a function of a company’s financial condition (tranquil, normal, and volatile REIT prices). We show that the size of risk spillovers is more than twice as large when REITs are in financial distress and find evidence for the impact of geographical proximity: REITs that have their properties located in close distance to the properties of other REITs show risk spillovers that are on average 33% higher than REITs that have similar properties but at a larger distance. We estimate the risk gradient to decrease nonlinearly and to have zero slope for property distances of more than 250 miles. Our empirical findings provide first empirical evidence on the transmission of risk spillovers from underlying real positions to the securitized level of a company.
Specifically, our results provide new insights concerning the relevance of geographical diversification for REITs and have important implications for the investment and risk management decisions of real estate investors, mortgage lenders, home suppliers, and policy makers.REITs, fundamental value, geographic diversification, information diffusion, risk
spillovers, state-dependent sensitivity VaR (SDSVaR)Adams, ZenoFüss, RolandSchindler, Felix0-0-20152015Adams, Z., Füss, R., & Schindler, F. (2015). The Sources of Risk Spillovers Among U.S. REITs: Financial Characteristics and Regional Proximity. Real Estate Economics, 43(1), 67-100, DOI:10.1111/1540-6229.12060.https://www.alexandria.unisg.ch/export/DL/226013.pdfnoneFinancialization in Commodity Markets: Disentangling the Crisis from the Style Effectworking paperhttps://www.alexandria.unisg.ch/publications/234616enIn this paper, we show that large inflows into commodity investments, a recent phenomenon known as financialization, has changed the behavior and dependence structure between commodities and the general stock market. The common perception is that the increase in comovements is the result of distressed investors selling both assets during the 2007-2009 financial crisis. We show that financial distress alone cannot explain the size and persistence of comovements. Instead, we argue that commodities have become an investment style for institutional investors. Given that institutional investors continue to target funds into commodities, we predict spillovers between commodities and the stock market to remain high in the future.Financialization; commodities; risk spillovers; style investing; state-dependent sensitivity VaRAdams, ZenoGlück, Thorsten20142014Adams, Z., & Glück, T., SoF - HSG (Eds.), (2014). Financialization in Commodity Markets: Disentangling the Crisis from the Style Effect. School of Finance Working Paper Series. St. Gallen: SoF - HSG.https://www.alexandria.unisg.ch/export/DL/234617.pdfnoneSpillover Effects among Financial Institutions : A State-Dependent Sensitivity Value-at-Risk (SDSVaR) Approachjournal paperhttps://www.alexandria.unisg.ch/publications/217575enurn:ISSN:0022-1090Journal of Financial and Quantitative AnalysisIn this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days.Risk spillovers; state-dependent sensitivity value-at-risk (SDSVaR); quantile regression; financial institutions; hedge fundsAdams, ZenoFüss, RolandGropp, Reint0-0-20142014Adams, Z., Füss, R., & Gropp, R. (2014). Spillover Effects among Financial Institutions: A State-Dependent Sensitivity Value-at-Risk (SDSVaR) Approach. Journal of Financial and Quantitative Analysis, 49(3), 575-598.noneDisentangling the Short and Long-Run Effects of Occupied Stock in the Rental Adjustment Processjournal paperhttps://www.alexandria.unisg.ch/publications/217582enurn:ISSN:0895-5638doi:10.1007/s11146-010-9250-7Journal of Real Estate Finance and EconomicsIn the current stand of literature on the rental adjustment process starting with Hendershott et al. (Real Estate Economics, 30, 165-183, 2002a, Journal of Real Estate Finance and Economics, 24, 59-87, 2002b) it has become practice to treat the compound variable “occupied stock” as a supply variable. In this study we show that this variable deserves a more critical investigation and that the general view of a supply variable may be misleading. Using panel data covering 30 urban areas for 17 years, we investigate the rental adjustment process in the German office market. The application of recently developed cointegration techniques for non-stationary panel data in conjunction with the corresponding error correction model (ECM) enables us to overcome the data limitations, particularly existent for most European real estate markets. Hence, our primary motivation is (a) to demonstrate how “occupied stock” should be interpreted correctly and (b) to provide useful insights into the long-term relationships and short-run dynamics of real office prime rents. The empirical evidence suggests that a one percent rise in office employment increases real rents on average by 1.64% through higher demand for office space. On the other hand, a one percent increase in the supply of office space decreases real rents in the long run by 2.25%. The results from the error correction model show that deviations from the long-run equilibrium lead to an adjustment process which restores equilibrium within approximately 3 years.Panel cointegration analysis – FMOLS regression – Error Correction Model – Urban rent models – German office marketAdams, ZenoFüss, Roland0-05-20122012Adams, Z., & Füss, R. (2012). Disentangling the Short and Long-Run Effects of Occupied Stock in the Rental Adjustment Process. Journal of Real Estate Finance and Economics, 44(04/2012), 570-590, DOI:10.1007/s11146-010-9250-7.noneInvestment choice and performance potential in the mutual fund industryjournal paperhttps://www.alexandria.unisg.ch/publications/217579enurn:ISSN:1470-8272doi:10.1057/jam.2012.1Journal of Asset ManagementThis article investigates the performance potential of a set of three investment choices: multi-asset, multi-management and multi-instrument. These approaches have been used recently in the asset management industry to give investors access to an extended investment universe, and to provide higher risk-adjusted returns to clients. In this context, we evaluate each investment choice's overall contribution to portfolio performance. Using bootstrapping simulations and a set of performance measures over a 20-year sample, we show that: 1. extending the typical equity- and bond-focused fund to a set of five asset classes increases the Sharpe ratio by 50 per cent on average, 2. allowing for third-party funds in a client's portfolio significantly reduces company-specific risk, and 3. including single assets leads to an increased return potential for skilled portfolio managers. Thus, our empirical results suggest that investments in actively managed mutual funds are likely to benefit significantly from these multi-investment approaches, and that the current practice of providing investors with balanced in-house funds is suboptimal.Portfolio management; investment strategies; performance evaluation; mutual funds; bootstrapping.Adams, ZenoFüss, RolandWohlschieß, Volker0-04-20122012Adams, Z., Füss, R., & Wohlschieß, V. (2012). Investment choice and performance potential in the mutual fund industry. Journal of Asset Management, 13(02/2012), 84-101, DOI:10.1057/jam.2012.1.noneCross Hedging Jet-Fuel Price Exposurejournal paperhttps://www.alexandria.unisg.ch/publications/217577enurn:ISSN:0140-9883doi:10.1016/j.eneco.2012.06.011Energy EconomicsThis paper investigates the cross hedging performance of several oil forwards contracts using WTI, Brent, gasoil and heating oil to manage jet-fuel spot price exposure. We apply three econometric techniques that have been widely tested and applied in the cross hedging literature on foreign exchange and stock index futures markets. Using quotes from the financial industry on forward contracts, we can show that the optimal cross hedging instrument depends on the maturity of the instrument’s forwards contract. The results highlight that the standard approach in the literature to use crude oil as a cross hedge is not optimal. By contrast, for short hedging horizons our results indicate that gasoil forwards contracts represent the highest cross hedging efficiency for jet-fuel spot price exposure, while for maturities of more than three months, the predominance of gasoil diminishes in comparison to WTI and Brent.Cross-hedging; hedge ratio; futures & forwards; crude oil; error correction model.Adams, ZenoGerner, Mathias0-09-20122012Adams, Z., & Gerner, M. (2012). Cross Hedging Jet-Fuel Price Exposure. Energy Economics, 34(05/2012), 1301-1309, DOI:10.1016/j.eneco.2012.06.011.noneThe Predictive Power of Value-at-Risk Models in Commodity Futures Marketsjournal paperhttps://www.alexandria.unisg.ch/publications/217584enurn:ISSN:1470-8272doi:10.1057/jam.2009.21Journal of Asset ManagementApplying standard value-at-risk (VaR) models to assets with non-normally distributed returns can lead to an underestimation of the true risk. Commodity futures returns are driven by continuous supply and demand shocks that lead to a distinct pattern of time-varying volatility. As a result of these specific risk characteristics, commodity returns create the ideal environment for testing the accuracy of VaR models. Therefore, this article examines the in- and out-of-sample performance of various VaR approaches for commodity futures investments. Our results suggest that dynamic VaR models such as the CAViaR and the GARCH-type VaR generally outperform traditional VaRs. These models can adequately incorporate the time-varying volatility of commodity returns, and are sensitive to significant changes in the series of commodity returns. This has important implications for the risk management of portfolios involving commodity futures positions. Risk managers willing to familiarize themselves with these complex models are rewarded with a VaR that shows the adequate level of risk even under extreme and rapidly changing market conditions, as well as under calm market periods, during which excessive capital reserves would lead to unnecessary opportunity costs.commodities; risk management; value-at-risk (VaR); GARCH modeling; conditional autoregressive value-at-risk (CAViaR); quantile regressionFüss, RolandAdams, ZenoKaiser, Dieter K.0-10-20102010Füss, R., Adams, Z., & Kaiser, D. K. (2010). The Predictive Power of Value-at-Risk Models in Commodity Futures Markets. Journal of Asset Management, 11(4), 261-285, DOI:10.1057/jam.2009.21.noneMacroeconomic Determinants of International Housing Marketsjournal paperhttps://www.alexandria.unisg.ch/publications/217583enurn:ISSN:1051-1377doi:10.1016/j.jhe.2009.10.005Journal of Housing EconomicsThis paper examines the long-term impact and short-term dynamics of macroeconomic variables on international housing prices. Since adequate housing market data are generally not available and usually of low frequency we apply a panel cointegration analysis consisting of 15 countries over a period of 30 years. Pooling the observations allows us to overcome the data restrictions which researchers face when testing long-term relationships among single real estate time series. This study does not only confirm results from previous studies, but also allows for a comparison of single country estimations in an integrated equilibrium framework. The empirical results indicate house prices to increase in the long-run by 0.6% in response to a 1% increase in economic activity while construction costs and the long-term interest rate show average long-term effects of approximately 0.6% and ?0.3%, respectively. Contrary to current literature our estimates suggest only about 16% adjustment per year. Thus the time to full recovery may be much slower than previously stated, so that deviations from the long-term equilibrium result in a dynamic adjustment process that may take up to 14 years.Adams, ZenoFüss, Roland01-03-20102010Adams, Z., & Füss, R. (2010). Macroeconomic Determinants of International Housing Markets. Journal of Housing Economics, 19(1), 38-50, DOI:10.1016/j.jhe.2009.10.005.noneValue at Risk, GARCH Modelling and the Forecasting of Hedge Fund Return Volatilityjournal paperhttps://www.alexandria.unisg.ch/publications/217586enurn:ISSN:1074-1240Journal of Derivatives and Hedge FundsThis paper examines the conditional volatility characteristics of daily management style returns and compares the out-of-sample forecasts of different Value at Risk (VaR) approaches, namely, the normal, Cornish–Fisher (CF), and the so-called GARCH-type VaR. The examination of the conditional volatility of hedge fund styles and composite returns shows important differences concerning persistence, mean reversion and asymmetry in the period under consideration. Hedge fund returns exhibit significant negative skewness and excess kurtosis, which cannot be captured in the normal VaR whereas the CF-VaR results in a systematic downward shift of the conventional VaR. The GARCH-type VaR, however, includes the time-varying conditional volatility and is able to trace the actual return process more effectively. Since the forecast performance cannot detect which of the three VaR types can match the time-varying risk adequately, an adjusted hit ratio takes the size of the hits as well as the average VaR into account. According to this, the GARCH-type VaR outperforms the other VaRs for most of the hedge fund style indices.hedge funds, Value at Risk, GARCH models, forecastingFüss, RolandKaiser, Dieter K.Adams, Zeno10-01-20072007Füss, R., Kaiser, D. K., & Adams, Z. (2007). Value at Risk, GARCH Modelling and the Forecasting of Hedge Fund Return Volatility. Journal of Derivatives and Hedge Funds, 13(1), 2-25.none